microRNAs are endogenous small noncoding RNAs that regulate gene expression negatively at posttranscriptional level. This latest addition to the complex gene regulatory circuitry revolutionizes our way to understanding physiological and pathological processes in the human body. Here we investigated the possible role of microRNAs in the development of multidrug resistance (MDR) in gastric cancer cells. microRNA expression profiling revealed a limited set of microRNAs with altered expression in multidrugresistant gastric cancer cell line SGC7901/VCR compared to its parental SGC7901 cell line. Among the downregulated microRNAs are miR-15b and miR-16, members of miR-15/16 family, whose expression was further validated by qRT-PCR. In vitro drug sensitivity assay demonstrated that overexpression of miR15b or miR-16 sensitized SGC7901/VCR cells to anticancer drugs whereas inhibition of them using antisense oligonucleotides conferred SGC7901 cells MDR. The downregulation of miR-15b and miR-16 in SGC7901/VCR cells was concurrent with the upregulation of Bcl-2 protein. Enforced mir-15b or miR-16 expression reduced Bcl-2 protein level and the luciferase activity of a BCL2 3 0 untranslated region-based reporter construct in SGC7901/VCR cells, suggesting that BCL2 is a direct target of miR-15b and miR-16. Moreover, overexpression of miR-15b or miR-16 could sensitize SGC7901/VCR cells to VCR-induced apoptosis. Taken together, our findings suggest that miR-15b and miR-16 could play a role in the development of MDR in gastric cancer cells at least in part by modulation of apoptosis via targeting BCL2.

Unlike other biometrics such as fingerprints and face, the distinct aspect of iris comes from randomly distributed features. This leads to its high reliability for personal identification, and at the same time, the difficulty in effectively representing such details in an image. This paper describes an efficient algorithm for iris recognition by characterizing key local variations. The basic idea is that local sharp variation points, denoting the appearing or vanishing of an important image structure, are utilized to represent the characteristics of the iris. The whole procedure of feature extraction includes two steps: 1) a set of one-dimensional intensity signals is constructed to effectively characterize the most important information of the original two-dimensional image; 2) using a particular class of wavelets, a position sequence of local sharp variation points in such signals is recorded as features. We also present a fast matching scheme based on exclusive OR operation to compute the similarity between a pair of position sequences. Experimental results on 2255 iris images show that the performance of the proposed method is encouraging and comparable to the best iris recognition algorithm found in the current literature.

This paper presents a novel iris coding method based on differences of discrete cosine transform (DCT) coefficients of overlapped angular patches from normalized iris images. The feature extraction capabilities of the DCT are optimized on the two largest publicly available iris image data sets, 2,156 images of 308 eyes from the CASIA database and 2,955 images of 150 eyes from the Bath database. On this data, we achieve 100 percent Correct Recognition Rate (CRR) and perfect Receiver-Operating Characteristic (ROC) Curves with no registered false accepts or rejects. Individual feature bit and patch position parameters are optimized for matching through a product-of-sum approach to Hamming distance calculation. For verification, a variable threshold is applied to the distance metric and the False Acceptance Rate (FAR) and False Rejection Rate (FRR) are recorded. A new worst-case metric is proposed for predicting practical system performance in the absence of matching failures, and the worst case theoretical Equal Error Rate (EER) is predicted to be as low as 2.59 x 10(-4) on the available data sets.